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ISSN No: 2456

International

Research

Real-Time Face Detection Security System Haar Classifier Method

Ei Mon Phyo1Assistant Lecturer

Department of Electronic Engineering ABSTRACT Face Detection is concerned with finding whether or not there are any faces in a given images. Security and surveillance are the two important aspects of human being. Face detection is very important because it is not being safe in human environment. So, FDetection Security System is essential between individual in life. In the modern world everything is changed to provide a better life. So we were decided to develop the Real Time Face detection system. The importance of the face detection as it is essesurveillance and real user interfaces security to the country. Face differ in skin colour, nose, eyebrows, chin between different people in humanity. In this paper we effort is to develop system about face detection security system in the real time. KEYWORD: Haar classifier, Segmentation and Edge/Line Detection, Face Detection, FeatureExtraction, Computer Vision 1. INTRODUCTION In the modern world of living, new technologevolved every day. The most popular and widely use banking system, considering for smart higher security for superior life standard. Security System based on Face Detection and GSM for technology, which can be used in Banks, Security Offices and Hogiving protection to expensive possessions. In this system, only the authorized person the valuable things like money, licenses and jewels from locker.time human identification systems are security, surveillance and biometric applications. 2. COMPUTER VISION Computer Vision is a library of programming functions mainly aimed at real-time computer vision.

@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 5 | Jul-Aug 2018

ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume

International Journal of Trend in Scientific

Research and Development (IJTSRD)

International Open Access Journal

Face Detection Security System uHaar Classifier Method

Ei Mon Phyo1, Nay Win Zaw2, Aye Aye Tun3 Assistant Lecturer, 2Professor, 3Lecturer

f Electronic Engineering, West Yangon Technological University, Myanmar

Face Detection is concerned with finding whether or not there are any faces in a given images. Security and surveillance are the two important aspects of human being. Face detection is very important because it is not being safe in human environment. So, Face Detection Security System is essential between individual in life. In the modern world everything is changed to provide a better life. So we were decided to develop the Real Time Face detection system. The importance of the face detection as it is essential for surveillance and real user interfaces security to the country. Face differ in skin colour, nose, eyebrows, chin between different people in humanity. In this paper we effort is to develop system about face

me.

Haar classifier, Segmentation and Edge/Line Detection, Face Detection, Feature

technologies are . The most popular and widely use

banking system, considering for smart higher security for superior life standard. Security System based on

technology, which can Offices and Homes for

giving protection to expensive possessions. In this valuable things

like money, licenses and jewels from locker. Real important for lications. [2]

is a library of programming time computer vision.

In simple language it is library used for Image Processing. It is mainly used to do all the operation related to Images. The goal of Computer Vision is to emulate human vision using digital images through three main processing components, executed one after the other: Image acquisition, Ianalysis and Understandingclosely linked with artificial intelligence, as the computer must interpret what it sees, and then perform appropriate analysis. [3 3. HAAR CLASSIFIER 3.1 What is Haar Classifier?Haar Cascade is basically a classifier which is used to detect the object for which it has been trained forfrom the source. The Haar Cascadepositive image over a set of negative images.cascade is an object detection algorithm. It is used to locate faces, pedestrians, objects and facial expressions in an image. Among them mainly used for face detection.[6] 3.2 Haar-like features

Figure 1.Five Haar like features

The size and position of the pattern’s support can very provide its blank and white rectangles have the same dimension, border each other and keep their relative positions. Haar-like features are digital image features used in object recognition. They owe their name to

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Scientific

(IJTSRD)

International Open Access Journal

using

angon Technological University, Myanmar

In simple language it is library used for Image Processing. It is mainly used to do all the operation

l of Computer Vision is to emulate human vision using digital images through three main processing components, executed one after

Image processing Image nderstanding. Computer vision is

cial intelligence, as the computer must interpret what it sees, and then

. [3]

What is Haar Classifier? is basically a classifier which is used to

detect the object for which it has been trained for, Haar Cascade is by covering the

over a set of negative images. Haar cascade is an object detection algorithm. It is used to locate faces, pedestrians, objects and facial expressions in an image. Among them mainly used

Figure 1.Five Haar like features

The size and position of the pattern’s support can very provide its blank and white rectangles have the same dimension, border each other and keep their relative

like features are digital image features recognition. They owe their name to

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their intuitive similarity with Haar wavelets and were used in the first real-time face detector. [5] 4. FEATURES EXTRACTION Transformation of input data into a set ofeatures. Features are distinctive properties of input patterns that help in different between the categories of an input pattern. Feature extraction involves reducing the amount of resources required to describe a large set of data. [1]

Figure2. Example of Feature Extraction

4.1 Segmentation and Edge/Line DetectionThe image analysis process requires us to take vast amounts of low level pixel data and extract information. Image segmentation is find regions that represent objects or meaningful parts of objects. Division of the image into regions corresobjects of interest is necessary before any processing can be done at a level higher than that of the pixelIdentification of real objects, pseudoshadows, or actually finding anything of within the image, requires some form of segmentation. Edge detection methods are used as a first step in the line detection process. Edge detection is also used to find complex object boundaries by marpotential edge of points corresponding to places in an image where rapid changes in brightness occur. After these edge points have been marked, they can be merged to form lines and object outlines. Oftenpeople are confused about the difference between an edge and a line. [7]

Figure2. Example of Segmentation and Edge/Line Detection

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their intuitive similarity with Haar wavelets and were [5]

Transformation of input data into a set of Features are distinctive properties of input

between the categories Feature extraction involves

reducing the amount of resources required to describe

mple of Feature Extraction

Segmentation and Edge/Line Detection The image analysis process requires us to take vast

ts of low level pixel data and extract find regions that f objects.

Division of the image into regions corresponding to is necessary before any processing

can be done at a level higher than that of the pixel. Identification of real objects, pseudo-objects,

ng of interest within the image, requires some form of

Edge detection methods are used as a first step in the tion is also used to

find complex object boundaries by marking the ponding to places in an brightness occur. After

edge points have been marked, they can be merged to form lines and object outlines. Often people are confused about the difference between an

Segmentation and Edge/Line

5. FACE DETECTION The system is using Haar Classifier Method. We have developed three algorithms, for face detection given image, from a folder of images and for real time face detection. The following face detection system.

Figure3. (a) Face Detection by using Haar Classifier Method

Figure3. (b) Face Detection by using Haar Classifier Method

Figure3. (c) Face Detection by using Haar Classifier Method

5.1 Aadvantages of by usin

Method � Haar-like classifier features

image features used in object recognition� A Haar-like feature considers adjacent rectangular

regions at a specific location in a detection window, sums up the pixel intensitregion and calculates the difference between these sums.

� Advantage of a Haar-like feature over most other features is its calculation speed. Due to the use of integral images, a Haarcan be calculated in constant time (approximately

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Aug 2018 Page: 2454

The system is using Haar Classifier Method. We have developed three algorithms, for face detection from a

a folder of images and for real time following below figure 3 is for

3. (a) Face Detection by using Haar

Classifier Method

3. (b) Face Detection by using Haar

Classifier Method

3. (c) Face Detection by using Haar

Classifier Method

of by using Haar Classifier

like classifier features are digital object recognition.

like feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these

like feature over most other features is its calculation speed. Due to the use

, a Haar-like feature of any size calculated in constant time (approximately

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470

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60 microprocessor instructions for a 2-rectangle feature).

� The main of cascading is achieving a good performance for both accuracy and time-complexity, when you have a number of weak classifier.

� The weak classifier must work at least with 51% accuracy. The cascade classifier rejects the many of samples in first node classifier with an efficient time.

� High detection accuracy and low false positive rate.[4]

6. EQUIPMENT OF EXPERIMENTAL TEST

Figure4. (a) Equipment of Test

. Figure4. (b) Equipment of Test

7. TEST AND RESULTS In this section, tests and results of face detection and classification system using Haar Classifier Method are shown expressed. The performance of the algorithm as a whole is analyzed and discussed below the figure. The image pre-processing is shown in Figure 5.

(a)

(b)

Figure5. (a) Open File of Data, (b) Programming of Face Detection by using Haar Classifier Method

Figure6. (a) Test and result of Face Detection by

using Haar Classifier

Figure6. (b) Test and result of Face Detection by

using Haar Classifier 8. CONCLUSION The smart security system has been aimed to design in such a way that it can fulfil the needs of the users for surveillance security area. Face detection security is very useful and essential important in many living together in the world. Using Haar Classifier Method, we have worse knowledge and sharing interface between human and computer software today. 9. ACKNOWLEDGEMENTS Firstly, the author would like to acknowledge Dr. Kyi Soe, Rector of West Yangon Technological University, for his kind permission to carry out this research work. The authors would like to express their thanks to all persons who have given support during the preparation period of this paper. The authors

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470

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would like to thank many colleagues from digital image processing research group of Department of Electronic Engineering of West Yangon Technological University. The author particularly wishes to acknowledge (Head of Electronic Department) Dr. Nay Win Zaw, my supervisor Daw Aye Aye Tun, my teacher U Myat Htut Zaw and all the teachers from Department of Electronic Engineering, West Yangon Technological University, for their support, encouragement and invaluable guidance in preparation of this research. The authors would like to express their thanks to all persons who have given support during the period of this research work. 10. REFERENCE 1. https://en.wikiwpedia.org/iki/feature_extractio

2. K.Raju1& Dr. Y. Srinivasa Rao “Real time Implementation of Face Recognition System on

Raspberry Pi” International Journal of Engineering & Technology, 7 (2.17) (2018) 85-89

3. https://en.wikipedia.org/wiki/Computer_vision

4. https://en.wikipedia.org/wiki/Haarlike_feature

5. Vandna Singh1, Dr. Vinod Shokeen2, Bhupendra Singh3 “Face Detection by Haar Cascade Classifier with Simple and Complex Backgrounds Images Using Open CV Implementation” International Journal of Advanced Technology in Engineering and Science, Volume No.01, Issue No. 12, December 2013

6. Nora Krasner, “Real-time face detection with Haar cascades”

7. SCOTT E UMBAUGH “Digital Image Processing and Analysis” “Human and Computer Vision with CVIP tools second edition”